publication . Article . Other literature type . 2008

Predicting defect-prone software modules using support vector machines

Elish, Karim O.; Elish, Mahmoud O.;
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  • Published: 01 May 2008 Journal: Journal of Systems and Software, volume 81, pages 649-660 (issn: 0164-1212, Copyright policy)
  • Publisher: Elsevier BV
Abstract
Effective prediction of defect-prone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Support vector machines (SVM) have been successfully applied for solving both classification and regression problems in many applications. This paper evaluates the capability of SVM in predicting defect-prone software modules and compares its prediction performance against eight statistical and machine learning models in the context of four NASA datasets. The results indicate that the prediction performance of SVM is generally better than, or at least, is competitive against the compared mo...
Subjects
free text keywords: Machine learning, computer.software_genre, computer, Software verification and validation, Software construction, Relevance vector machine, Data mining, Software metric, Software sizing, Computer science, Support vector machine, Structured support vector machine, Artificial intelligence, business.industry, business, Software
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publication . Article . Other literature type . 2008

Predicting defect-prone software modules using support vector machines

Elish, Karim O.; Elish, Mahmoud O.;